Targeted Infusion Project: Infusing deep learning into the undergraduate computer science and engineering curricula
- Institution
- Tuskegee University, AL
- Principal investigator
- Xiao Chang
- Funding
- $400K
- Duration
- 2023–2026
Moonbase score
Still being scored
Abstract
The Historically Black Colleges and Universities Undergraduate Program (HBCU-UP) through Targeted Infusion Projects supports the development, implementation, and study of evidence-based, innovative models and approaches for improving the preparation and success of HBCU undergraduate students so that they may pursue science, technology, engineering, or mathematics (STEM) graduate programs and/or careers. The goal of this project is to infuse self-contained deep learning modules into existing undergraduate computer science and engineering classes at Tuskegee University (TU). This project aims to prepare undergraduate students for deep learning/AI workforce and advanced graduate programs by developing deep learning curriculum modules composed of lecture slides, demos, hands-on labs, homework assignments, and quizzes. The curriculum modules will be incorporated into a deep learning/machine learning focused course, and three elective computer science and engineering courses. In addition, introductory deep learning workshops will be developed and open to all students at TU. The project will also support academic year and summer research experiences, and facilitate faculty professional development through consultation, annual onsite faculty summer workshops, and webinars. The open-source educational materials and videos will be made available on GitHub and YouTube for dissemination, and seminars offered to K-12 students to raise awareness of deep learning and AI. This project will contribute to the national need for artificial intelligence research and workforce development to strategically foster an AI-ready workforce in America. This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.
Research team
- Xiao ChangPI
- Li Jiang
- Fan Wu
- Jay Bhuyan
- Kai S Koong
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